Model Extractor Callback — mlr3.model

This CallbackResample extracts information from the model after training with a user-defined function. This way information can be extracted from the model without saving the model (store_models = FALSE). The fun must be a function that takes a learner as input and returns the extracted information as named list (see example). The callback is very helpful to call $selected_features(), $importance(), $oob_error() on the learner.

Arguments

fun: (function(learner))
Function to extract information from the learner. The function must have the argument learner. The function must return a named list.

Examples

task = tsk("pima")
learner = lrn("classif.rpart")
resampling = rsmp("cv", folds = 3)

# define function to extract selected features
selected_features = function(learner) list(selected_features = learner$selected_features())

# create callback
callback = clbk("mlr3.model_extractor", fun = selected_features)

rr = resample(task, learner, resampling = resampling, store_models = FALSE, callbacks = callback)

rr$data_extra
#> Key: <uhash, iteration>
#>                                   uhash iteration data_extra
#>                                  <char>     <int>     <list>
#> 1: 7b434a39-2aae-471c-bd1e-3e560a5ca256         1  <list[1]>
#> 2: 7b434a39-2aae-471c-bd1e-3e560a5ca256         2  <list[1]>
#> 3: 7b434a39-2aae-471c-bd1e-3e560a5ca256         3  <list[1]>